测量地雷压力机动力学 通过增强的YOLOv8模型和数学建模来测量地雷压力机动力学
Rui Zhao1,2, Rong Cong1, Ruijie Zhou1
1College of Physical Education, Yunnan Agricultural University, Kunming 650201, China.
Sensors (Basel, Switzerland)
|February 27, 2026
概括
一个新的基于视觉的系统准确地测量了没有物理传感器的地雷压力动力学. 这种非接触式方法为力量训练和研究提供了传统线性位置传感器 (LPT) 的可靠替代方案.
科学领域:
- 生物力学 生物力学
- 计算机视觉 计算机视觉
- 体育科学 运动科学 运动科学
背景情况:
- 地雷压力是一个关键的上体强度评估,但当前的监测工具,如线性位置传感器 (LPTs) 有局限性.
- LPTs需要物理附件,使用专有软件,并且由于传感器漂移和噪音,在高负载下可能不准确.
研究的目的:
- 开发和验证一种无标记,无接触,基于视觉的系统,用于测量地雷压力运动.
- 为了克服传统的LPTs在评估上半身推力方面的限制.
主要方法:
- 开发了一个基于视觉的系统,使用了增强的YOLOv8-OBB模型,具有极化自我注意力机制,C3k2模块和SPPF结构.
- 集成了一个数学建模框架来计算地雷压力集中阶段的动力学指标.
- 在247个试验中,对视觉系统的测量与线性位置传感器 (LPT) 设备 (GymAware) 进行了比较,用于各种负载 (20-35公斤).
主要成果:
- 视觉系统实现了高检测准确度 (mAP@0.5的0.995) 的杆目标.
- 在视觉系统和LPT之间发现了强烈的相关性 (r > 0.85),用于峰值速度,平均速度,峰值功率和平均功率.
- 视觉系统在高负载下显示出与LPTs相似的稳定性,并显示出可预测的速度高估.
结论:
- 开发的基于视觉的系统为监测地雷压力动力学提供了可靠和实用的替代方案.
- 这种非接触式方法比LPT具有优势,特别是在高负载条件下减轻与传感器相关的错误.
- 该系统适用于力量训练应用和需要准确的动力学数据的科学研究.
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